On approximating the eigenvalues and eigenvectors of linear continuous operators

8 years ago

(original), eigenvalue/eigenvector problems, html, Numerical Analysis, paper, Schulz type iterations, semilocal convergence, solving F(x)=0 iteratively

Abstract

We consider the computing of an eigenpair (an eigenvector $v=(v^{(i)})_{i=1,n}$ and an eigenvalue $\lambda$) of a matrix $A\in\mathbb{R}^{n\times n}$, by considering a supplementary condition (we call it norming function for the eigenvector), represented by a polynomial of degree 2. A usual choice is
\begin{equation}
G(v):={\textstyle\frac12} \sum_{i=1}^n( v^{(i)})^2-1=0.
\end{equation}
We propose here a new choice:
\begin{equation}
G(v):={\textstyle\frac1{2n}} \sum_{i=1}^n( v^{(i)})^2-1=0,
\end{equation}
which has the advantage that leads to a smaller nonlinearity.

Indeed, for the $n+1$-dimensional nonlinear system (a polynomial equation of degree 2) we obtain:
\[
F\left( x\right) :={{Av-\lambda v} \choose {G(v)-1}}
=0,
\]
and our proposed choice leads to a second derivative of $F$ that has norm 2 (compared to $n$, for the usual choice).

We consider this problem in a general setting, of a linear continuous operator $A:V\rightarrow V$, $V$ Banach space, which leads to a polynomial equation of degree two.

We study the semilocal convergence of an iterative method of Schultz type for this problem; this method has the advantage that does not require the solving of a linear system at each iteration step:
\begin{align*}
x_{k+1} =&x_k-\Gamma_kF\left( x_k\right) \\
\Gamma_{k+1} =&\Gamma_k\left( 2I-F^{\prime }\left( x_{k+1}\right) \Gamma_k\right) , \qquad k=0,1,\ldots,
\end{align*}
where $x_0\in X$, $\Gamma_0\in \mathcal{L}\left( X\right)$, and $I$ is the identity operator of $\mathcal{L}\left( X\right)$.

We obtain semilocal convergence conditions which show that the method has r-convergence order 2.

Authors

Emil Cătinaş
(Tiberiu Popoviciu Institute of Numerical Analysis)

Ion Păvăloiu
(Tiberiu Popoviciu Institute of Numerical Analysis)

Keywords

linear continuous operator; Banach space; Newton type method; eigenvector; eigenvalue; eigenpair; Schultz method; r-convergence order.

Cite this paper as:

E. Cătinaş, I. Păvăloiu, On approximating the eigenvalues and eigenvectors of linear continuous operators, Rev. Anal. Numér. Théor. Approx., 26 (1997) nos. 1-2, pp. 19-27.

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1222-9024

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2457-8126

Google Scholar citations

[1] M.P. Anselone, L.B. Rall, The solution of characteristic value-vector problems by Newton method, Numer. Math. 11 (1968), 38–45.
CrossRef

[2] E. Catinas, I. Pavaloiu, On the Chebyshev method for approximating the eigenvalues of linear operators, Rev. Anal. Numer. Theor. Approx. 25 (1996) nos. 1–2, pp. 43–56.
article post, article on the journal website

[3] P.G. Ciarlet, Introduction a l’analyse numerique matricielle et a l’optimisation, Mason, Paris, 1990.

[4] F. Chatelin, Valeurs propres de matrices, Mason, Paris, 1988.

[5] L. Collatz, Functionalanalysis und Numerische Mathematik, Springer-Verlag, Berlin, 1964.

[6] A. Diaconu, On the convergence of an iterative proceeding of Chebyshev type, Rev. Anal. Numer. Theor. Approx. 24 (1995) nos. 1–2, pp. 91–102.
article on the journal website

[7] A. Diaconu, I. Pavaloiu, Sur quelque methodes iteratives pour la resolution des equations operationelles, Rev. Anal. Numer. Theor. Approx. 1 (1972) no. 1, pp. 45–61.
article on the journal website

[8] V.S. Kartısov, F.L. Iuhno, O nekotorıh Modifikatiah Metoda Niutona dlea Resenia Nelineinoi Spektralnoi Zadaci, J. Vıcisl. matem. i matem. fiz. 33 (1973) 9, pp. 1403–1409.

[9] I. Lazar, On a Newton type method, Rev. Anal. Numer. Theor. Approx. 23 (1994) no. 2, pp. 167–174.
article on journal website

[10] I. Pavaloiu, Observations concerning some approximation methods for the solutions of operator equations, Rev. Anal. Numer. Theor. Approx. 23 (1994) no. 2, pp. 185–196.
article on the journal website

[11] I. Pavaloiu, Sur les procedes iteratifs a un ordre eleve de convergence, Mathematica (Cluj) 12 (35) (1970) no. 2, pp. 309–324.
post

[12] R.A. Tapia, L.D. Whitley, The projected Newton method has order 1+√2 for the symmetric eigenvalue problem, SIAM J. Numer. Anal. 25 (1988) no. 6, pp. 1376–1382.
CrossRef

[13] F.J. Traub, Iterative Methods for the Solution of Equations, Prentice-Hall Inc., Englewood Cliffs, N. J., 1964.

[14] S. Ul’m, On the iterative method with simultaneous approximation of the inverse of the operator, Izv. Acad. Nauk. Estonskoi S.S.R. 16 (1967) no. 4, pp. 403–411.

[15] T. Yamamoto, Error bounds for computed eigenvalues and eigenvectors, Numer. Math. 34 (1980), pp. 189–199.
CrossRef

Paper (preprint) in HTML form

On approximating the eigenvalues and eigenvectors of linear continuous operators

Emil Cătinaş and Ion Păvăloiu

1991 AMS Subject Classification: 65J20, 65H17.

1. Introduction

One of the difficulties that appear when solving numerically the operatorial equations by iterative methods having high convergence orders is that at each iteration step there must be solved a linear operatorial equation, or, in some cases, even more than one [3].

In a series of papers ([7], [8], [10], [15]) there has been proposed the elimination of this inconvenience by the simultaneous generation of two sequences: a sequence approximating the solution, and a sequence of linear operators approximating the inverse of the linear operator appearing at each step in the linear equation.

In the present paper we shall study this approach when the solutions of the equation yield eigenvalues and eigenvectors of a linear operator. It is well known that all the derivatives of order higher than three of the attached operator are the null multilinear operators. The convergence results may also be applied to polynomial operator equations of degree two.

The r convergence order of the sequence approximating the solution is proved to be 2.

The operatorial equation which lead us to the approximation of the eigenvalues and eigenvectors of a linear operator can be constructed in the following way (see [6]):

Let $V$ be a Banach space over the field $\mathbb{K}$ (where $\mathbb{K}=\mathbb{C}$ or $\mathbb{K}=\mathbb{R}$ ); denote by $\mathcal{L}(V)$ the set of linear continuous operators acting from $V$ into $V$ and let $A\in\mathcal{L}(V)$ . The scalar $\lambda\in\mathbb{K}$ is an eigenvalue of $A$ if the equation

(1)

Av-\lambda v=\theta

has at least a solution $v\neq\theta$ , called an eigenvector of $A$ corresponding to $\lambda$ , where $\theta$ is the null element of $V$ .

For the determination of an eigenpair $(v,\lambda)$ we also consider another equation

(2)

G(v)-1=0,

where $G:V\rightarrow\mathbb{K}$ is a polynomial functional of degree at most two (a norming function) for which $G(0)\neq 1$ .

Remark.

The functional $G$ may also be taken as a linear continuous functional, but in this case, if $\operatorname{dim}V=n$ , then $\operatorname{dim}\operatorname{ker}G=n-1$ , so there exist eigenvectors which do not fulfill equation (2). ∎

Denote the Banach space $X=V\times\mathbb{K}$ and for $x=\tbinom{v}{\lambda}$ , $v\in V$ , $\lambda\in\mathbb{K}$ define

\left\|x\right\|=\max\left\{\left\|v\right\|,\left|\lambda\right|\right\}.

Considering the operator $F:X\rightarrow X$ given by

F\left(x\right)=\binom{Av-\lambda v}{G(v)-1}=\binom{(A-\lambda I)v}{G(v)-1},

and denoting by $\bar{\theta}=\binom{\theta}{0}$ the null element of $X$ , then the operatorial equation for which a solution yields an eigenpair of $A$ is

(3)

F\left(x\right)=\bar{\theta}.

It is known that the Fréchet derivatives of $F$ are (see [6]):

F^{\prime}\left(x_{0}\right)h=\left(\begin{array}[]{cc}A-\lambda_{0}I&-v_{0}\\ G^{\prime}(v_{0})&0\end{array}\right)\left(\begin{array}[]{c}u\\ \alpha\end{array}\right)=\left(\begin{array}[]{c}Au-\lambda_{0}u-\alpha v_{0}% \\ G^{\prime}(v_{0})u\end{array}\right),

and

F^{\prime\prime}\left(x_{0}\right)hk=\left(\begin{array}[]{c}-\alpha w-\beta u% \\ G^{\prime\prime}uw\end{array}\right),

where $x_{0}=\binom{v_{0}}{\lambda_{0}},$ $h=\binom{u}{\alpha}$ , $k=\binom{w}{\beta}\in X.$

It is obvious that $F^{\left(i\right)}\left(x_{0}\right)h_{1}\dots h_{i}=\bar{\theta},$ for all $i\geq 3$ and $x_{0},h_{1},\dots,h_{i}\in X$ , and also that $\|F^{\prime\prime}\|=\max\{2,\|G^{\prime\prime}\|\}.$

For such an operator the following identity holds:

(4)

F\left(y\right)=F\left(x\right)+F^{\prime}\left(x\right)\left(y-x\right)+F^{% \prime\prime}\left(x\right)\left(y-x\right)^{2},\qquad\forall x,y\in X,

where the bilinear operator $F^{\prime\prime}$ does not depend on $x$ .

We shall consider two sequences $\left(x_{k}\right)_{k\geq 0},$ $\left(\Gamma_{k}\right)_{k\geq 0}$ having elements from $X,$ resp. $\mathcal{L}\left(X\right),$ using the following iterative method:

(5)		$\displaystyle x_{k+1}$	$\displaystyle=$	$\displaystyle x_{k}-\Gamma_{k}F\left(x_{k}\right)$
	$\displaystyle\Gamma_{k+1}$	$\displaystyle=$	$\displaystyle\Gamma_{k}\left(2I-F^{\prime}\left(x_{k+1}\right)\Gamma_{k}\right% ),\qquad k=0,1,\ldots,$

where $x_{0}\in X,$ $\Gamma_{0}\in\mathcal{L}\left(X\right)$ , and $I$ is the identity operator of $\mathcal{L}\left(X\right)$ .

We call (5) the combined Newton method. In the following sections we shall study its convergence when $F$ is a polynomial operator of degree 2 and we shall apply it for the approximation of the eigenvalues and eigenvectors of matrices.

2. The convergence of the combined Newton method

For the study of the convergence of the method (5) we shall use the following lemma:

Lemma 1.

If the sequences $\left(\delta_{k}\right)_{k\geq 0}$ and $\left(\rho_{k}\right)_{k\geq 0}$ of real positive numbers satisfy the following conditions

	$\displaystyle\delta_{k+1}$	$\displaystyle\leq\left(\delta_{k}+2\rho_{k}\right)^{2}$
	$\displaystyle\rho_{k+1}$	$\displaystyle\leq\rho_{k}\delta_{k}+\rho_{k}^{2},\qquad k=0,1,\ldots$

where $\max\left\{\delta_{0},\rho_{0}\right\}\leq\tfrac{1}{9}d,$ for some $d<1,$ then the following relations hold:

	$\displaystyle\delta_{k}$	$\displaystyle\leq\tfrac{1}{9}d^{2^{k}},$
	$\displaystyle\rho_{k}$	$\displaystyle\leq\tfrac{1}{9}d^{2^{k}},\qquad k=0,1,\ldots$

The proof of this lemma is immediately obtained by induction.

Let $x_{0}\in X$ , $\Gamma_{0}\in{\mathcal{L}}(X)$ and $S=\{x\in X\;|\;\|x-x_{0}\|\leq r\},$ $r>0.$ Suppose that the following estimation holds:

\left\|F^{\prime\prime}\left(x\right)\right\|\leq K,\qquad\forall x\in S.

The following theorem holds.

Theorem 2.

If $x_{0}\in S,$ $\Gamma_{0}\in\mathcal{L}\left(X\right),$ $r$ and $F$ satisfy the following conditions

a)

there exists $F^{\prime}\left(x_{0}\right)^{-1}$ and $b_{0}=\|F^{\prime}\left(x_{0}\right)^{-1}\|;$
b)

$q=Kb_{0}r<1;$
c)

$\max\left\{\delta_{0},\rho_{0}\right\}\leq\frac{1}{9}d,$ for some $d<1,$ where

$\displaystyle\delta_{0}$ $\displaystyle=\|I-F^{\prime}\left(x_{0}\right)\Gamma_{0}\|,$

$\displaystyle\rho_{0}$ $\displaystyle=\tfrac{50}{81}b^{2}K\|F\left(x_{0}\right)\|,\qquad and$

$\displaystyle b$ $\displaystyle=\tfrac{b_{0}}{1-q};$
d)

$\frac{d}{5K\left(1-q\right)}\leq r,$

then the following properties hold:

1)

the sequences $\left(x_{k}\right)_{k\geq 0},$ $\left(\Gamma_{k}\right)_{k\geq 0}$ generated by (5) converge, and $x_{k}\in S$ for all $k\geq 0;$
2)

denoting $x^{\ast}=\lim_{k\rightarrow\infty}x_{k}$ and $\Gamma^{\ast}=\lim_{k\rightarrow\infty}\Gamma_{k}$ , then $F\left(x^{\ast}\right)=\bar{\theta}$ and $\Gamma^{\ast}=F^{\prime}\left(x^{\ast}\right)^{-1};$
3)

the following relations are true:

$\displaystyle\left\|x^{\ast}-x_{k}\right\|$ $\displaystyle\leq\frac{d^{2^{k}}}{5bK\left(1-d^{2^{k}}\right)},\qquad k=0,1,\ldots;$

$\displaystyle\left\|\Gamma^{\ast}-\Gamma_{k}\right\|$ $\displaystyle\leq\frac{d^{2^{k}}}{3\left(1-d^{2^{k}}\right)},\qquad k=0,1,\ldots$

Proof.

At first we shall show that for any $x\in S$ there exists $F^{\prime}\left(x\right)^{-1}$ and $\|F^{\prime}\left(x\right)^{-1}\|\leq b.$

Indeed, under the above assumptions, it easily follows that

\|I-\Gamma_{0}F^{\prime}\left(x\right)\|\leq Kb_{0}r=q<1.

Applying the Banach lemma we get

\|F^{\prime}\left(x\right)^{-1}\|\leq\tfrac{b_{0}}{1-q}=b.

Taking into account a) and b) it follows that

(6)

\left\|\Gamma_{0}\right\|\leq\|F^{\prime}(x_{0})^{-1}\|\big(\|F^{\prime}(x_{0}% )\Gamma_{0}-I\|+1\big)\leq b_{0}(1+\delta_{0})\leq\tfrac{10}{9}b_{0}\leq\tfrac% {10}{9}b,

which, together with the first relation from (5), for $k=0,$ imply

\left\|x_{1}-x_{0}\right\|\leq\left\|\Gamma_{0}\right\|\left\|F\left(x_{0}% \right)\right\|\leq\tfrac{10\cdot 81bd}{9\cdot 9\cdot 50b^{2}K}<\tfrac{d}{5bK% \left(1-d\right)}\leq r,

i.e. $x_{1}\in S.$

Denote $\rho_{1}=\frac{50}{81}b^{2}K\|F(x_{1})\|$ and $\delta_{1}=\|I-F^{\prime}(x_{1})\Gamma_{1}\|.$ An elementary reasoning shows that

	$\displaystyle\rho_{1}$	$\displaystyle\leq\rho_{0}^{2}+\delta_{0}\rho_{0}$
	$\displaystyle\delta_{1}$	$\displaystyle\leq(\delta_{0}+2\rho_{0})^{2},$

whence, by the above Lemma and by relation c) we infer that

	$\displaystyle\rho_{1}$	$\displaystyle\leq\tfrac{1}{9}d^{2}$
	$\displaystyle\delta_{1}$	$\displaystyle\leq\tfrac{1}{9}d^{2}.$

Suppose now that the following relations hold:

$\alpha)$ $x_{1},\ldots,x_{k}\in S;$

$\beta)$ $\delta_{i}=\|I-F^{\prime}(x_{i})\Gamma_{i}\|\leq\frac{1}{9}d^{2^{i}}$ , $\rho_{i}=\frac{50}{81}b^{2}K\|F(x_{i})\|\leq\frac{1}{9}d^{2^{i}}$ , $i=\overline{0,k};$

$\gamma)$ $\|x_{i}-x_{i-1}\|\leq\frac{d^{2^{i}}}{5bK},$ $i=\overline{1,k}.$

We shall show that they also hold for $k+1$ .

The inequality

\left\|\Gamma_{k}\right\|\leq\tfrac{10}{9}b

is proved similarly to (2.2). Using also the second relation from (5) we get

\left\|x_{k+1}-x_{k}\right\|\leq\|\Gamma_{k}\|\|F(x_{k})\|\leq\tfrac{d^{2^{k}}% }{5bK},

i.e. the relation $\gamma)$ for $i=k+1.$

Further,

\left\|x_{k+1}-x_{0}\right\|\leq\sum_{i=0}^{k}\|x_{i+1}-x_{i}\|\leq\tfrac{1}{5% 6K}\sum_{i=0}^{k}d^{2^{k}}<\tfrac{d}{5Kb(1-d)}\leq r,

whence it follows that $x_{k+1}\in S.$

Denoting $\rho_{k+1}=\frac{50}{81}b^{2}K\|F(x_{k+1})\|$ and $\delta_{k+1}=\|I-F^{\prime}(x_{k+1})\Gamma_{k+1}\|,$ then

	$\displaystyle\rho_{k+1}$	$\displaystyle\leq\rho_{k}^{2}+\rho_{k}\delta_{k}$
	$\displaystyle\delta_{k+1}$	$\displaystyle\leq\left(\delta_{k}+\rho_{k}\right)^{2},$

whence, by our Lemma we get

	$\displaystyle\rho_{k+1}$	$\displaystyle\leq\tfrac{1}{9}d^{2^{k+1}}$
	$\displaystyle\delta_{k+1}$	$\displaystyle\leq\tfrac{1}{9}d^{2^{k+1}},$

i.e. the relation $\beta)$ for $i=k+1.$

It is obvious that $x_{i}\in S$ for all $i=1,2,\ldots$ and the relations $\beta)$ and $\gamma)$ hold for all $k\in\mathbb{N}.$

From the inequalities

\left\|x_{k+m}-x_{k}\right\|\leq\sum_{i=k}^{k+m-1}\left\|x_{i+1}-x_{i}\right\|% \leq\frac{d^{2^{k}}}{5Kb(1-d^{2^{k}})},\qquad m=1,2,\ldots

it follows that the sequence $\left(x_{k}\right)_{k\geq 0}$ converges. Denoting $x^{\ast}=\lim_{k\rightarrow\infty}x_{k}$ then from the last relation for $m\rightarrow\infty$ we get

\left\|x^{\ast}-x_{k}\right\|\leq\frac{d^{2^{k}}}{5Kb(1-d^{2^{k}})},\qquad k=0% ,1,\ldots

From the second relation of (5) it follows that

	$\displaystyle\\|\Gamma_{k+1}-\Gamma_{k}\\|$	$\displaystyle=\\|I-F^{\prime}\left(x_{k+1}\right)\Gamma_{k}\\|$
		$\displaystyle\leq\\|I-F^{\prime}(x_{k})\Gamma_{k}\\|+\\|F^{\prime}(x_{k})-F^{% \prime}(x_{k+1})\\|\\|\Gamma_{k}\\|$
		$\displaystyle\leq\delta_{k}+\\|\Gamma_{k}\\|^{2}K\\|F(x_{k})\\|$
		$\displaystyle\leq\delta_{k}+2\rho_{k}\leq\tfrac{1}{3}d^{2^{k}}.$

The previous inequality implies that the sequence $\left(\Gamma_{k}\right)_{k\geq 0}$ converges and the relations $2)$ and the second inequality in $3)$ hold. ∎

3. The approximation of eigenvalues and eigenvectors of matrices

In the following we shall apply the studied method for the approximation of the eigenvalues and eigenvectors of complex matrices.

Let $A=\left(a_{ij}\right)_{i,j=1,n}\in\mathcal{M}_{n}\left(\mathbb{K}\right)$ be a square matrix with the elements $a_{i,j}\in\mathbb{K}.$ Consider $V=\mathbb{K}^{n}$ and $X=\mathbb{K}^{n+1}.$ In this case the equation (3) is written

F_{i}(x)=F_{i}\big(x^{(1)},...,x^{(n+1)}\big)=0,\qquad i=\overline{1,n+1},

where

F_{i}\left(x\right)=a_{i1}x^{(1)}+\ldots+a_{i,i-1}x^{(i-1)}+(a_{ii}-x^{(n+1)})% x^{(i)}+a_{i,i+1}x^{(i+1)}+\ldots+a_{in}x^{(n)},

$i=\overline{1,n}.$

For the norming function we can take $G=F_{n+1}$ , where

(7)

F_{n+1}(x)=\tfrac{1}{2}\sum_{i=1}^{n}(x^{(i)})^{2}-1.

The first and second order derivatives of $F$ are given by

F^{\prime}\left(x\right)h=\!\left(\!\!\!\begin{array}[]{ccccccc}a_{11}-x^{(n+1% )}&a_{12}&\ldots&a_{1n}&-x^{(1)}\\ a_{21}&a_{22}-x^{(n+1)}&\ldots&a_{2n}&-x^{(2)}\\ \vdots&\vdots&&\vdots&\vdots\\ a_{n1}&a_{n2}&\ldots&a_{nn}-x^{(n+1)}&-x^{(n)}\\ x^{(1)}&x^{(2)}&\ldots&x^{(n)}&0\end{array}\!\!\!\right)\!\!\left(\!\!\begin{% array}[]{c}h^{(1)}\\ h^{(2)}\\ \vdots\\ h^{(n)}\\ \!h^{(n+1)}\!\end{array}\!\!\!\!\right)\!,

and

F^{\prime\prime}\left(x\right)hk=\left(\begin{array}[]{ccccc}-k^{(n+1)}&0&% \ldots&0&-k^{(1)}\\ 0&-k^{(n+1)}&\ldots&0&-k^{(2)}\\ \vdots&\vdots&&\vdots&\vdots\\ 0&0&\ldots&-k^{(n+1)}&-k^{(n)}\\ k^{(1)}&k^{(2)}&\ldots&k^{(n)}&0\end{array}\right)\left(\begin{array}[]{c}h^{(% 1)}\\ h^{(2)}\\ \vdots\\ h^{(n)}\\ h^{(n+1)}\end{array}\right).

where $x=\big(x^{(1)},\ldots,x^{(n+1)}\big),h=\big(h^{(1)},\ldots,h^{(n+1)}\big),k=% \big(k^{(1)},\ldots,k^{(n+1)}\big)\in\mathbb{K}^{n+1}.$

Suppose that $\mathbb{K}^{n+1}$ is equipped with the max-norm. Then, from the above formula it follows that $\|F^{\prime\prime}\left(x\right)\|=n$ , for all $x\in\mathbb{K}^{n+1}.$

Another possible choice for the norming function $G$ is

(8)

F_{n+1}(x)=\tfrac{1}{2n}\sum_{i=1}^{n}(x^{(i)})^{2}-1.

in which case we get $\|F^{\prime\prime}\left(x\right)\|=2$ , for all $x\in\mathbb{K}^{n+1}.$

The Theorem stated in the previous section can be reformulated according to this setting.

Remark.

In [16] it is proved that for a given eigenpair $(v,\lambda)$ the operator $F^{\prime}(v,\lambda)$ is nonsingular iff $\lambda$ is simple. Hence our result applies only for such eigenvalues.

4. Numerical example

Consider the real matrix

A=\left(\begin{array}[]{ccc}1&1&0.5\\ 1&1&0.25\\ 0.5&0.25&2\end{array}\right)

which has the following eigenvalues and eigenvectors

\begin{array}[]{cc}\lambda_{1}=-0.01664728361&v_{1}=\left(-0.7212071298,0.6863% 492877,0.09372796349\right)\\ \lambda_{2}=1.480121423&v_{2}=\left(-0.4442810581,-0.5621094204,0.6976011330% \right)\\ \lambda_{3}=2.536525860&v_{3}=\left(0.5314834119,0.4614733520,0.7103293096% \right).\end{array}

Taking the initial values $x_{0}=\left(-0.7,0.6,0.09,-0.01\right)$ , and applying the studied method for $G$ given by (7), we obtain the following results:

${\footnotesize k}$	${\footnotesize x}_{1}$	${\footnotesize x}_{2}$	${\footnotesize x}_{3}$	${\footnotesize x}_{4}{\footnotesize=\lambda}$
${\footnotesize 0}$	${\footnotesize-0.7}$	${\footnotesize 0.6}$	${\footnotesize 0.09}$	${\footnotesize-0.01}$
${\footnotesize 1}$	${\footnotesize\numprint{-0.7241759075}}$	${\footnotesize\numprint{0.6894343346}}$	${\footnotesize\numprint{0.09406959984}}$	${\footnotesize\numprint{-0.01611417648}}$
${\footnotesize 2}$	${\footnotesize\numprint{-0.7212379135}}$	${\footnotesize\numprint{0.6863753479}}$	${\footnotesize\numprint{0.09373253857}}$	${\footnotesize\numprint{-0.01663490068}}$
${\footnotesize 3}$	${\footnotesize\numprint{-0.7212071340}}$	${\footnotesize\numprint{0.6863492908}}$	${\footnotesize\numprint{0.09372796420}}$	${\footnotesize\numprint{-0.01664727974}}$
${\footnotesize 4}$	${\footnotesize\numprint{-0.7212071298}}$	${\footnotesize\numprint{0.6863492877}}$	${\footnotesize\numprint{0.09372796350}}$	${\footnotesize\numprint{-0.01664728361}}$

For the initial values $x_{0}=\left(-1.7,1.6,0.2,-0.01\right)$ , and taking $G$ given by (8) we obtain:

${\footnotesize k}$	${\footnotesize x}_{1}$	${\footnotesize x}_{2}$	${\footnotesize x}_{3}$	${\footnotesize x}_{4}{\footnotesize=\lambda}$
${\footnotesize 0}$	${\footnotesize-1.7}$	${\footnotesize 1.6}$	${\footnotesize 0.2}$	${\footnotesize-0.01}$
${\footnotesize 1}$	${\footnotesize\numprint{-1.768398012}}$	${\footnotesize\numprint{1.682966665}}$	${\footnotesize\numprint{0.2298835780}}$	${\footnotesize\numprint{-0.01695674174}}$
${\footnotesize 2}$	${\footnotesize\numprint{-1.766594350}}$	${\footnotesize\numprint{1.681210330}}$	${\footnotesize\numprint{0.2295865533}}$	${\footnotesize\numprint{-0.01664900983}}$
${\footnotesize 3}$	${\footnotesize\numprint{-1.766589467}}$	${\footnotesize\numprint{1.681205540}}$	${\footnotesize\numprint{0.2295856852}}$	${\footnotesize\numprint{-0.01664728365}}$

References

[1]
[2] M.P. Anselone, L.B. Rall, The solution of characteristic value-vector problems by Newton method, Numer. Math. 11 (1968), 38–45.
[3] E. Cătinas, I. Păvăloiu, On the Chebyshev method for approximating the eigenvalues of linear operators, Rev. Anal. Numér. Théor. Approx. 25 (1996) nos. 1–2, pp. 43–56.
[4] P.G. Ciarlet, Introduction à l’analyse numérique matricielle et à l’optimisation, Mason, Paris, 1990.
[5] F. Chatelin, Valeurs propres de matrices, Mason, Paris, 1988.
[6] L. Collatz, Functionalanalysis und Numerische Mathematik, Springer-Verlag, Berlin, 1964.
[7] A. Diaconu, On the convergence of an iterative proceeding of Chebyshev type, Rev. Anal. Numér. Théor. Approx. 24 (1995) nos. 1–2, pp. 91–102.
[8] A. Diaconu, I. Păvăloiu, Sur quelque méthodes itératives pour la résolution des équations opérationelles, Rev. Anal. Numér. Théor. Approx. 1 (1972) no. 1, pp. 45–61.
[9] V.S. Kartîşov, F.L. Iuhno, O nekotorîh Modifikaţiah Metoda Niutona dlea Resenia Nelineinoi Spektralnoi Zadaci, J. Vîcisl. matem. i matem. fiz. 33 (1973) 9, pp. 1403–1409.
[10] I. Lazăr, On a Newton type method, Rev. Anal. Numér. Théor. Approx. 23 (1994) no. 2, pp. 167–174.
[11] I. Păvăloiu, Observations concerning some approximation methods for the solutions of operator equations, Rev. Anal. Numér. Théor. Approx. 23 (1994) no. 2, pp. 185–196.
[12] I. Păvăloiu, Sur les procédés itératifs à un ordre élevé de convergence, Mathematica (Cluj) 12 (35) (1970) no. 2, pp. 309–324.
[13] R.A. Tapia, L.D. Whitley, The projected Newton method has order 1+ $\sqrt{2}$ for the symmetric eigenvalue problem, SIAM J. Numer. Anal. 25 (1988) no. 6, pp. 1376–1382.
[14] F.J. Traub, Iterative Methods for the Solution of Equations, Prentice-Hall Inc., Englewood Cliffs, N. J., 1964.
[15] S. Ul’m, On the iterative method with simultaneous approximation of the inverse of the operator, Izv. Acad. Nauk. Estonskoi S.S.R. 16 (1967) no. 4, pp. 403–411.
[16] T. Yamamoto, Error bounds for computed eigenvalues and eigenvectors, Numer. Math. 34 (1980), pp. 189–199.

Received August 15, 1996. ”Tiberiu Popoviciu” Institute of Numerical Analysis

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	$\displaystyle\\|\Gamma_{k+1}-\Gamma_{k}\\|$	$\displaystyle=\\|I-F^{\prime}\left(x_{k+1}\right)\Gamma_{k}\\|$
		$\displaystyle\leq\\|I-F^{\prime}(x_{k})\Gamma_{k}\\|+\\|F^{\prime}(x_{k})-F^{% \prime}(x_{k+1})\\|\\|\Gamma_{k}\\|$
		$\displaystyle\leq\delta_{k}+\\|\Gamma_{k}\\|^{2}K\\|F(x_{k})\\|$
		$\displaystyle\leq\delta_{k}+2\rho_{k}\leq\tfrac{1}{3}d^{2^{k}}.$

	$\displaystyle\delta_{0}$	$\displaystyle=\\|I-F^{\prime}\left(x_{0}\right)\Gamma_{0}\\|,$
	$\displaystyle\rho_{0}$	$\displaystyle=\tfrac{50}{81}b^{2}K\\|F\left(x_{0}\right)\\|,\qquad and$
	$\displaystyle b$	$\displaystyle=\tfrac{b_{0}}{1-q};$

	$\displaystyle\left\\|x^{\ast}-x_{k}\right\\|$	$\displaystyle\leq\frac{d^{2^{k}}}{5bK\left(1-d^{2^{k}}\right)},\qquad k=0,1,\ldots;$
	$\displaystyle\left\\|\Gamma^{\ast}-\Gamma_{k}\right\\|$	$\displaystyle\leq\frac{d^{2^{k}}}{3\left(1-d^{2^{k}}\right)},\qquad k=0,1,\ldots$

On approximating the eigenvalues and eigenvectors of linear continuous operators

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On approximating the eigenvalues and eigenvectors of linear continuous operators

1. Introduction

Remark.

2. The convergence of the combined Newton method

Lemma 1.

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Proof.

3. The approximation of eigenvalues and eigenvectors of matrices

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4. Numerical example

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